A methodological review of national and transnational pharmaceutical budget impact analysis guidelines for new drug submissions
Bibliographic record
Abstract
INTRODUCTION: Budget impact analysis (BIA) in health care, sometimes referred to as resource impact, is the financial change in the use of health resources associated with adding a new drug to a formulary or the adoption of a new health technology. Several national and transnational organizations worldwide have updated their BIA guidelines in the past 4 years. The aim of the present review was to provide a comprehensive list of the key recommendations of BIA guidelines from different countries that may be of interest for those who wish to build or to update BIA guidelines. METHODS: National and transnational BIA guidelines were searched in databases including MEDLINE, EMBASE, Cochrane, EconLit, CINAHL, Business Source Premier, HealthSTAR, and the gray literature including regulatory agency websites. Data were reviewed and abstracted based on key elements in a standard BIA model (analytical model structure, input and data sources, and reporting format). RESULTS: Eight national (Australia, UK, Belgium, Ireland, France, Poland, Brazil, and Canada) and one transnational (International Society for Pharmacoeconomics and Outcomes Research) BIA guidelines were included in this review, and a comprehensive list of BIA recommendations was identified. The review showed that certain recommendations such as patient population assessment, drug-related direct costs, discounting, and disaggregated results were common across the various jurisdictions. BIA guidelines differed from each other in terms of the number and scope of recommendations, the terminology used (eg, the definition of comparators or cost offsets) and the direction of the recommendations (ie, to include or not to include with respect to such items as off-label indications, indirect costs, clinical outcomes, and resource utilization). CONCLUSION: While there was a common purpose for all of the BIA guidelines that were identified, substantial differences did occur in the specific recommendations. The pharmaceutical financing system structure might explain why guidelines from the UK, Australia, and Canada have more country-specific recommendations. The desire to be consistent with adopted economic evaluation assumptions might be another reason for some observed differences between countries. Further research is required to assess the source of the heterogeneity between BIA recommendations are identified in different guidelines.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.415 | 0.693 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.061 | 0.068 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".